MoML: Online Meta Adaptation for 3D Human Motion Prediction

In the academic field, the research on human motion pre-diction tasks mainly focuses on exploiting the observed in-formation to forecast human movements accurately in the near future horizon. However, a significant gap appears when it comes to the application field, as current models are all trained offline, with fixed parameters that are inher-ently suboptimal to handle the complex yet ever-changing nature of human behaviors. To bridge this gap, in this pa-per, we introduce the task of online meta adaptation for hu-man motion prediction, based on the insight that finding “smart weights” capable of swift adjustments to suit dif-ferent motion contexts along the time is a key to improving predictive accuracy. We propose MoML, which ingeniously borrows the bilevel optimization spirit of model-agnostic meta-learning, to transform previous predictive mistakes into strong inductive biases to guide online adaptation. This is achieved by our MoAdapter blocks that can learn er-ror information by facilitating efficient adaptation via a few gradient steps, which fine-tunes our meta-learned “smart” initialization produced by the generic predictor. Considering real-time requirements in practice, we further propose Fast-MoML, a more efficient variant of MoML that features a closed-form solution instead of conventional gradient up-date. Experimental results show that our approach can ef-fectively bring many existing offline motion prediction mod-els online, and improves their predictive accuracy.